@article {40,
	title = {Seeking Multiple Solutions: an Updated Survey on Niching Methods and Their Applications},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {PP},
	year = {2016},
	pages = {1-1},
	abstract = {Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.},
	keywords = {Benchmark testing, evolutionary computation, Meta-heuristics, Multi-modal optimization, Multi-solution methods, Niching methods, Optimization methods, Problem-solving, Sociology, Statistics, Swarm intelligence, Two dimensional displays},
	issn = {1089-778X},
	doi = {10.1109/TEVC.2016.2638437},
	author = {X. Li and M. G. Epitropakis and K. Deb and A. Engelbrecht}
}
@inbook {39,
	title = {Tutorials at PPSN 2016},
	booktitle = {Parallel Problem Solving from Nature {\textendash} PPSN XIV: 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings},
	year = {2016},
	pages = {1012{\textendash}1022},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field!},
	isbn = {978-3-319-45823-6},
	doi = {10.1007/978-3-319-45823-6_95},
	url = {http://dx.doi.org/10.1007/978-3-319-45823-6_95},
	author = {Doerr, Carola and Bredeche, Nicolas and Alba, Enrique and Bartz-Beielstein, Thomas and Brockhoff, Dimo and Doerr, Benjamin and Eiben, Gusz and Epitropakis, Michael G. and Fonseca, Carlos M. and Guerreiro, Andreia and Haasdijk, Evert and Heinerman, Jacqueline and Hubert, Julien and Lehre, Per Kristian and Malag{\`o}, Luigi and Merelo, J. J. and Miller, Julian and Naujoks, Boris and Oliveto, Pietro and Picek, Stjepan and Pillay, Nelishia and Preuss, Mike and Ryser-Welch, Patricia and Squillero, Giovanni and Stork, J{\"o}rg and Sudholt, Dirk and Tonda, Alberto and Whitley, Darrell and Zaefferer, Martin},
	editor = {Handl, Julia and Hart, Emma and Lewis, Peter R. and L{\'o}pez-Ib{\'a}{\~n}ez, Manuel and Ochoa, Gabriela and Paechter, Ben}
}
@conference {13,
	title = {Weighted Markov Chain Model for Musical Composer Identification},
	booktitle = {Applications of Evolutionary Computation},
	year = {2011},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {Several approaches based on the {\textquoteleft}Markov chain model{\textquoteright} have been proposed to tackle the composer identification task. In the paper at hand, we propose to capture phrasing structural information from inter onset and pitch intervals of pairs of consecutive notes in a musical piece, by incorporating this information into a weighted variation of a first order Markov chain model. Additionally, we propose an evolutionary procedure that automatically tunes the introduced weights and exploits the full potential of the proposed model for tackling the composer identification task between two composers. Initial experimental results on string quartets of Haydn, Mozart and Beethoven suggest that the proposed model performs well and can provide insights on the inter onset and pitch intervals on the considered musical collection.},
	doi = {10.1007/978-3-642-20520-0_34},
	author = {M. A. Kaliakatsos-Papakostas and M. G. Epitropakis and M. N. Vrahatis},
	editor = {Di Chio, Cecilia and Brabazon, Anthony and Di Caro, Gianni and Drechsler, Rolf and Farooq, Muddassar and Grahl, J{\"o}rn and Greenfield, Gary and Prins, Christian and Romero, Juan and Squillero, Giovanni and Tarantino, Ernesto and Tettamanzi, Andrea and Urquhart, Neil and Uyar, A.}
}
@conference {17,
	title = {Musical Composer Identification through Probabilistic and Feedforward Neural Networks},
	booktitle = {Applications of Evolutionary Computation},
	year = {2010},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	abstract = {During the last decade many efforts for music information retrieval have been made utilizing Computational Intelligence methods. Here, we examine the information capacity of the Dodecaphonic Trace Vector for composer classification and identification. To this end, we utilize Probabilistic Neural Networks for the construction of a similarity matrix of different composers and analyze the Dodecaphonic Trace Vector{\textquoteright}s ability to identify a composer through trained Feedforward Neural Networks. The training procedure is based on classical gradient-based methods as well as on the Differential Evolution algorithm. An experimental analysis on the pieces of seven classical composers is presented to gain insight about the most important strengths and weaknesses of the aforementioned approach.},
	doi = {10.1007/978-3-642-12242-2\_42},
	author = {M. A. Kaliakatsos-Papakostas and M. G. Epitropakis and M. N. Vrahatis},
	editor = {Di Chio, Cecilia and Brabazon, Anthony and Di Caro, Gianni and Ebner, Marc and Farooq, Muddassar and Fink, Andreas and Grahl, J{\"o}rn and Greenfield, Gary and Machado, Penousal and O{\textquoteright}Neill, Michael and Tarantino, Ernesto and Urquhart, Neil}
}
